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At present, deep learning has been widely applied in object detection, such as vehicle detection. In this paper, the deep learning EfficientDet model was analyzed, and its advantages in vehicle detection were confirmed. A phased adaptive training model was constructed to avoid local optimum in training process, then it was used to detect vehicles from both short and long distance. The detection results showed that compared with detection methods based on Cascade R-CNN and CenterNet, the proposed model was superior in terms of computational complexity, time con sumption and detection accuracy. Meanwhile, further analysis figured out the optimal detection distance and angle.Finally, an example is given to verify that the proposed method can be applied to a large range of vehicle detection. [ABSTRACT FROM AUTHOR]